Next Article in Journal
Effects of Mind–Body Movements on Balance Function in Stroke Survivors: A Meta-Analysis of Randomized Controlled Trials
Previous Article in Journal
Red-Light-Running Crashes’ Classification, Comparison, and Risk Analysis Based on General Estimates System (GES) Crash Database
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Int. J. Environ. Res. Public Health 2018, 15(6), 1291; https://doi.org/10.3390/ijerph15061291

Knowledge Discovery from Posts in Online Health Communities Using Unified Medical Language System

1
Department of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
2
Henley Business School, University of Reading, Reading RG6 6UD, UK
*
Author to whom correspondence should be addressed.
Received: 31 May 2018 / Revised: 15 June 2018 / Accepted: 16 June 2018 / Published: 19 June 2018
  |  
PDF [2189 KB, uploaded 19 June 2018]
  |  

Abstract

Patient-reported posts in Online Health Communities (OHCs) contain various valuable information that can help establish knowledge-based online support for online patients. However, utilizing these reports to improve online patient services in the absence of appropriate medical and healthcare expert knowledge is difficult. Thus, we propose a comprehensive knowledge discovery method that is based on the Unified Medical Language System for the analysis of narrative posts in OHCs. First, we propose a domain-knowledge support framework for OHCs to provide a basis for post analysis. Second, we develop a Knowledge-Involved Topic Modeling (KI-TM) method to extract and expand explicit knowledge within the text. We propose four metrics, namely, explicit knowledge rate, latent knowledge rate, knowledge correlation rate, and perplexity, for the evaluation of the KI-TM method. Our experimental results indicate that our proposed method outperforms existing methods in terms of providing knowledge support. Our method enhances knowledge support for online patients and can help develop intelligent OHCs in the future. View Full-Text
Keywords: online posts; online health communities; knowledge discovery; Unified Medical Language System; text mining online posts; online health communities; knowledge discovery; Unified Medical Language System; text mining
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Chen, D.; Zhang, R.; Liu, K.; Hou, L. Knowledge Discovery from Posts in Online Health Communities Using Unified Medical Language System. Int. J. Environ. Res. Public Health 2018, 15, 1291.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Int. J. Environ. Res. Public Health EISSN 1660-4601 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top